When time-series data are stored in a database management system (DMS), a non-limiting example of which is a high speed in-memory database system (IMDBS) such as the HANA architecture available from SAP (Walldorf, Germany), the majority of the storage required may be used for storing the timestamps associated with measurements. Because a typical time stamp value can require 4 to 6 bytes of storage space, very long series of time stamped data having a large number of measurements can require substantial storage space. For example, a utility provider (energy, water, telecommunications, etc.) operating “smart” meters for a million customers generates nearly 100 million data records in a day if each meter is sampled every 15 minutes. While various compression methods can be employed to reduce the storage requirements for data in the measurement columns, such approaches generally are not as useful for time stamp data, at least because the individual data values retained in such columns can be unique or nearly unique.
As a consequence, the data size can be extremely high for working with time-series data sets. For an IMDBS, large data sizes can require extremely high usage of main system memory and can be limiting or even prohibitive for certain in-memory operations.